Artificial intelligence supported valuation platform

ABSTRACT

Disclosed are method and systems to program a server to identify the value of a fund comprising shares of multiple private entities. The server receives transaction data associated with a fund where the transaction data identifies a proportion of shares within the fund associated with each private entity, price per share of each private entity, and other relevant data. The server then executes multiple artificial intelligence models to identify comparable public entities to each private entity. The server then retrieves stock price data for each public entity and calculates a value for each private entity in real time. The server also displays a value of the fund in real time where identification of each private entity is anonymized.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/774,829, filed Dec. 3, 2018, which is incorporated herein in itsentirety.

TECHNICAL FIELD

This application relates generally to generating, training, andoperating artificial intelligence models to achieve better valuations.

BACKGROUND

Conventional portfolio valuation methods are subjective, inaccurate, andtedious. In a conventional method, valuation of different portfoliosthat include shares of multiple companies is achieved using eachportfolio (or closed end funds) manager's subjective understanding ofthe market. For instance, a closed end fund manager examines whichcompanies are included within a portfolio, retrieves and monitorsrelevant market data, and determine a value for each company and theportfolio. This conventional method is undesirable because the portfoliovaluation is highly biased based on each portfolio manager's skills.Therefore, the above-mentioned method results in inconsistent and highlyunreliable portfolio valuations. Furthermore, this method is veryinefficient and time-consuming. In high-pressure and time-sensitiveenvironments, where many factors regarding a portfolio and itsunderlying companies may change within seconds, time plays an importantrole. However, existing methods have failed to provide timely,consistent, or accurate results.

As the processing power of computers has expanded, many have createdsoftware solutions to combat the above-mentioned technical challenges.However, the conventional technical solutions (e.g., software solutions)to the subjectivity, accuracy, and timeliness problems described abovehave also faced several technical shortcomings. For instance, manysoftware solutions use public information to predict market conditionsto evaluate a portfolio. For example, current clustering methods (e.g.,K nearest neighbor algorithms) identify a similar company (e.g., or themost similar company) to a given private entity and predict the value ofthe given company based on the identified similar company. While thesemethods are generally appropriate for clustering data point and foridentifying similarities between data points, existing artificialintelligence (AI) modeling methods are not suitable for portfoliovaluation platforms. First, existing AI modeling methods do not producehighly accurate results because they valuate companies based on othersimilar companies. Second, existing AI modeling technique may requirelarge processing power, which is costly and inefficient.

SUMMARY

For the aforementioned reasons, there is a desire for an artificialintelligence supported method utilizing an improved AI modelingtechnique to evaluate portfolios. What is desired is an AI modelingtechnique that is more efficient and produces more accurate results.What is also needed is a platform that displays company valuations in atimely manner.

In an embodiment, a method comprises receiving, by a server, transactiondata associated with a fund comprising a plurality of private entities,the transaction data corresponding to a proportion of the fundassociated with each private entity, shares purchased of each privateentity, and purchase price for the shares purchased of each privateentity; executing, by a server, a set of artificial intelligence modelsto identify a plurality of comparable public entities to each privateentity, the set of models comprising at least a first artificialintelligence model utilizing a learned distance k-nearest algorithm toidentify the plurality of comparable public entities, a secondartificial intelligence model utilizing a linear regression algorithm toidentify the plurality of comparable public entities, and a thirdartificial intelligence model utilizing a boosting tree regressionalgorithm to identify the plurality of comparable public entities;retrieving, by the server, financial data associated with the identifiedpublic entities; determining, by the server, a value for each privateentity based upon its respective identified plurality of publicentities; and displaying, by the server on a graphical user interface inreal time, an indicator of a value of the fund, the graphical userinterface comprising a value of each private entity within the fundwhere an identify of each private entity is anonymized.

In another embodiment, a computer system comprises one or moreelectronic data sources configured to store financial data associatedwith a set of public entities; and a server connected to the one or moreelectronic data sources, the server configured to receive transactiondata associated with a fund comprising a plurality of private entities,the transaction data corresponding to a proportion of the fundassociated with each private entity, shares purchased of each privateentity, and purchase price for the shares purchased of each privateentity; execute a set of artificial intelligence models to identify aplurality of comparable public entities to each private entity, the setof models comprising at least a first artificial intelligence modelutilizing a learned distance k-nearest algorithm to identify theplurality of comparable public entities, a second artificialintelligence model utilizing a linear regression algorithm to identifythe plurality of comparable public entities, and a third artificialintelligence model utilizing a boosting tree regression algorithm toidentify the plurality of comparable public entities; retrieve, from theone or more electronic data sources, financial data associated with theidentified public entities; determine a value for each private entitybased upon its respective identified plurality of public entities; anddisplay, on a graphical user interface in real time, an indicator of avalue of the fund, the graphical user interface comprising a value ofeach private entity within the fund where an identify of each privateentity is anonymized.

In another embodiment, a method comprises generating, by a server, anartificial intelligence model comprising a neural network correspondingto at least two sets of data points, each data point within a first setof data points corresponding to a first variable that is independent andeach data point within a second set of data points corresponding to asecond variable where each second variable is dependent upon acorresponding first variable; executing, by the server, a clusteringalgorithm to generate a plurality of clusters where each clustercorresponds to at least one data point within the set of data points;generating, by the computer, a training dataset comprising a third setof data points where each data point within the third set of data pointscorresponds to a pairwise distance between each two data points withinat least one cluster; and training, by the server, the artificialintelligence model based on the training dataset, wherein when thetrained artificial intelligence mode is executed using a new data pointhaving a first independent variable, the artificial intelligence modelidentifies a distance between the new data point and at least one datapoint within the cluster.

In another embodiment a computer system comprises a database configuredto store a training dataset; and a server in communication with thedatabase, wherein the server is configured to generate an artificialintelligence model comprising a neural network corresponding to at leasttwo sets of data points, each data point within a first set of datapoints corresponding to a first variable that is independent and eachdata point within a second set of data points corresponding to a secondvariable where each second variable is dependent upon a correspondingfirst variable; execute a clustering algorithm to generate a pluralityof clusters where each cluster corresponds to at least one data pointwithin the set of data points; generate a training dataset comprising athird set of data points where each data point within the third set ofdata points corresponds to a pairwise distance between each two datapoints within at least one cluster; and train the artificialintelligence model based on the training dataset, wherein when thetrained artificial intelligence mode is executed using a new data pointhaving a first independent variable, the artificial intelligence modelidentifies a distance between the new data point and at least one datapoint within the cluster.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting embodiments of the present disclosure are described by wayof example with reference to the accompanying figures, which areschematic and are not intended to be drawn to scale. Unless indicated asrepresenting the background art, the figures represent aspects of thedisclosure.

FIG. 1 illustrates components of an artificial intelligence (AI)supported valuation system, according to an embodiment.

FIG. 2 illustrates a flow diagram of a process executed in an AIsupported valuation system, according to an embodiment.

FIGS. 3A-B illustrate flow diagrams of a process executed by an AI modelto improve valuation accuracy, according to an embodiment.

FIGS. 4A-B illustrate conventional clustering methods, according to anembodiment.

FIG. 4C is a visual representation of identifying a nearest neighborgiven a dataset, in accordance with an embodiment.

FIG. 4D illustrates implementing the methods and systems describedherein on a portfolio of public entities, in accordance with anembodiment.

FIG. 5 illustrate a graphical user interface of an AI supportedvaluation system, according to an embodiment.

FIGS. 6-8 illustrate a graphical user interface of an AI supportedvaluation system, according to an embodiment.

DETAILED DESCRIPTION

Reference will now be made to the illustrative embodiments depicted inthe drawings, and specific language will be used here to describe thesame. It will nevertheless be understood that no limitation of the scopeof the claims or this disclosure is thereby intended. Alterations andfurther modifications of the inventive features illustrated herein, andadditional applications of the principles of the subject matterillustrated herein, which would occur to one skilled in the relevant artand having possession of this disclosure, are to be considered withinthe scope of the subject matter disclosed herein. Other embodiments maybe used and/or other changes may be made without departing from thespirit or scope of the present disclosure. The illustrative embodimentsdescribed in the detailed description are not meant to be limiting ofthe subject matter presented.

FIG. 1 illustrates components of an AI supported valuation system 100.The system 100 may include an analytics server 110 a, system database110 b, user computing devices 120 a-d (collectively user computingdevices 120), electronic data sources 140 a-c (collectively electronicdata source 140), and trading server 150. The above-mentioned componentsmay be connected to each other through a network 130. The examples ofthe network 130 may include, but are not limited to, private or publicLAN, WLAN, MAN, WAN, and the Internet. The network 130 may include bothwired and wireless communications according to one or more standardsand/or via one or more transport mediums.

The communication over the network 130 may be performed in accordancewith various communication protocols such as Transmission ControlProtocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP),and IEEE communication protocols. In one example, the network 130 mayinclude wireless communications according to Bluetooth specificationsets, or another standard or proprietary wireless communicationprotocol. In another example, the network 130 may also includecommunications over a cellular network, including, e.g., a GSM (GlobalSystem for Mobile Communications), CDMA (Code Division Multiple Access),EDGE (Enhanced Data for Global Evolution) network.

The system 100 is not confined to the components described herein andmay include additional or alternate components, not shown for brevity,which are to be considered within the scope of the embodiment.

The analytics server 110 a may generate and display a private equity(PE) platform graphical user interface (GUI) on each user computingdevice 120. An example of the trading GUI generated and hosted by theanalytics server 110 a may be a web-based application or a websiteconfigured to be displayed on different electronic devices, such asmobile devices, tablets, personal computer, and the like. The analyticsserver 110 a may host a website accessible to end-users, where thecontent presented via the various webpages may be controlled based uponeach particular user's role or viewing permissions. The analytics server110 a may be any computing device comprising a processor andnon-transitory machine-readable storage capable of executing the varioustasks and processes described herein. Non-limiting examples of suchcomputing devices may include workstation computers, laptop computers,server computers, laptop computers, and the like. While the system 100includes a single analytics server 110 a, in some configurations, theanalytics server 110 a may include any number of computing devicesoperating in a distributed computing environment.

The analytics server 110 a may execute software applications configuredto display the trading GUI (e.g., host a website), which may generateand serve various webpages to each user computing device 120. Differentusers operating the user computing devices 120 may use the website toview entity/portfolio valuations, transmit bids and/or purchaserequests.

In some implementations, the analytics server 110 a may be configured torequire user authentication based upon a set of user authorizationcredentials (e.g., username, password, biometrics, cryptographiccertificate, and the like). In such implementations, the analyticsserver 110 a may access the system database 110 b configured to storeuser credentials, which the analytics server 110 a may be configured toreference in order to determine whether a set of entered credentials(purportedly authenticating the user) match an appropriate set ofcredentials that identify and authenticate the user.

In some configurations, the analytics server 110 a may generate and hostwebpages based upon a particular user's role within the system 100(e.g., administrator, employee, and/or bidder). In such implementations,the user's role may be defined by data fields and input fields in userrecords stored in the system database 110 b. The analytics server 110 amay authentication the user and may identify the user's role byexecuting an access directory protocol (e.g. LDAP). The analytics server110 a may generate webpage content that customized according to theuser's role defined by the user record in the system database 110 b. Forinstance, a user may not have proper authorization to view certainentities valuations. In another example, a user may not be able totransmit purchase requests regarding one or more prohibited entities orone or more portfolios including certain prohibited private entities.

The electronic data sources 140 may represent various electronic datasources that contain data associated with private entities. Forinstance, database 140 c and third-party server 140 b may representdatabase/server having private or public market data associated withdifferent private entities. Non-limiting examples of database 140 c mayinclude databases continuously updated with market data (e.g., S&P 500).In some non-limiting examples, as described below, data associated withprivate entity may be inputted by a user operating the computing device140 a. For instance, an administrator may input various public ornonpublic data into a graphical user interface displayed on thecomputing device 140 a where the analytics server 110 a may use theinputted data to valuate a portfolio. In some embodiments, the analyticsserver 110 a may utilize the application programming interface (API) 140d to monitor market data within the electronic data sources 140.

User computing devices 120 may be any computing device comprising aprocessor and a non-transitory machine-readable storage medium capableof performing the various tasks and processes described herein.Non-limiting examples of a network node may be a workstation computer,laptop computer, tablet computer, and server computer. In operations,various users may use computing devices 120 to access the trading GUIoperationally managed by the analytics server 110 a. Using the tradingGUI, each computer may view valuation of different private entities anddifferent portfolios.

In operation, the analytics server 110 a may periodically scan datastored on the electronic data sources 140 and may retrieve attributesassociated with different public and private entities. The analyticsserver 110 a may also generate, train, and update an AI model configuredto use data retrieved from the electronic data sources 140 to generatean accurate valuation of one or more private entities. Upon executingthe AI model, the analytic server may populate a private equity (PE)platform GUI where different users operating computing devices 120 mayaccess the PE platform GUI to view different customizable valuations.

The analytics server 110 a may update the PE platform GUI in real timeor in near real time. As described below, the PE platform GUI maycomprise various price indicators (e.g., tickers) updated in real-timeor near real-time. The analytic server 110 a may provide users operatingcomputing devices 120 the option to customize the price indicators ordisplay the price indicators in relation to other customizable privateentities and or portfolios. As described below, price indicators aregraphical representations of the valuation of a private entity or aportfolio that may be anonymized.

Therefore, similar to public trading, a user operating the computingdevices 120 may also transmit a purchase order using the PE platformGUI. Upon receiving the purchase order, the analytics server 110 a maytransmit the purchase order, along with different attributes of thepurchase order such as amount, limit pricing, volume, call/optionpricing, and the like, to a trading server 150. The trading server 150may then facilitate a transaction using data received from the analyticsserver 110 a. For instance, the trading server 150 may transmit paymentsfrom the requesting party and may issue purchase confirmation once thetransaction is completed.

Even though the analytics server 110 a and the trading server 150 areshown as two separate computing devices/servers, it is expresslyunderstood that in some configurations, the analytics server 110 a mayperform the functionality described as being performed by the tradingserver 150. For instance, the trading server 150 may be a module of theanalytics server 110 a.

FIG. 2 illustrates a flow diagram of a process executed in an AIsupported valuation system, according to an embodiment. The method 200includes steps 200-250. However, other embodiments may includeadditional or alternative execution steps, or may omit one or more stepsaltogether. In addition, the method 200 is described as being executedby a server, similar to the analytics server described in FIG. 1.However, steps of method 200 may also be executed by any number ofcomputing devices operating in the distributed computing systemdescribed in FIG. 1. For instance, one or more user computing devicesmay locally perform part or all the steps described in FIG. 2.

Even though some aspects of the embodiments described herein aredescribed within the context of generic funds, it is expresslyunderstood that methods and systems described herein apply to all AIsupported financial valuations. For instance, in other embodiments, themethods and systems described herein may be applied to securities,valuations of public or private entities, bonds, stocks, and otherfinancial instruments and portfolios that include multiple instrumentsbelonging to diverse entities. The analytics server may utilize themethods and systems described herein to value private portfoliocompanies, private equity funds and then funds of private equity funds,which could be closed end funds, open ended funds, or other types ofpool investment vehicles.

At step 210, the analytics server may receive transaction dataassociated with one or more funds of private portfolio companies(hereafter referred to as “fund”). The transaction data may include anyfinancial/transaction data associated with an existing fund. The fundbeing evaluated may be an investment vehicle consisting a pool of moneycollected from many investors for investing in securities such asstocks, bonds, money market instruments and other assets (e.g., shareswithin one or more private entities). The fund may be operated by one ormore managers (administrator), who may allocate the fund's investmentsand attempt to produce capital gains and/or income for the fund'sinvestors. The fund's portfolio may be structured and maintained tomatch the investment objectives stated in its prospectus. The fund'sportfolio may include shares of private entities that are not publiclytraded. For instance, an administrator may acquire multiple shares of aprivate entity as an investment of the fund. Therefore, the “fund” mayrefer to (but not limited to) a shares of a private entity and/or aclosed end fund.

The transaction data may generally refer to information regarding thefund' portfolio and may include data associated with one or more privateentities within the fund's portfolio, such as a proportion of the fundassociated with each private entity, number of shares purchased fromeach private entity, price per share for each private entity, a value ofeach private entity estimated by an administrator of the fund.

In an embodiment, an administrator of a fund may login a web-basedapplication operated/provided by the analytics server to input theabove-described fund data. For instance, the administrator may login awebsite to access the analytics server and input various fund portfolioinformation. The website may include various prompts and inputcomponents (e.g., radio buttons, and text input fields) where theadministrator can input various attributes of one or more funds. Thereceived data may include a list of private entities (and a respectiveproportion of each private entity) within a fund. For instance, theadministrator may access the web-based application and indicate thatfund X comprises three private entities (entity A-C). The administratormay also input the following data:

TABLE 1 Number Number of Price Private Fund's of Shares Outstanding PerPrivate Entity Ratio Valuation owned Shares NAV Entity A 25% $1.0M 300010,000 $100 $300,000 B 25% $1.5M 5000 25,000 $60 $300,000 C 50% $12M 2505000 $2400 $600,000 Total $1.2M Value of Fund

As described in table 1, inputted data indicates that fund X includesthree private entities A-C. More specifically, the inputted dataindicates that fund X owns 1000 shares of private entity A, 5000 sharesof private entity B, and 250 shares of private entity C. Theadministrator further inputs a fair valuation of each private entity (atleast partially) owned by fund X. For instance, the administrator inputsthat administrators managing fund X have estimated private entity A tobe valued at $1 million, private entity B at $1.5 million, and privateentity C at $2.5 million. The administrator may also input a totalnumber of outstanding shares for each private entity as illustrated intable 1. For instance, private entity B may have 25,000 outstandingshares. In some other implementations, the analytics server may queryand retrieve the outstanding shares from public or private databases.

The administrator may also input (or the analytics server may calculate)a net asset value (NAV) associated with each private entity. A NAV mayrepresent a fund per-share market value. For instance, NAV may representa monetary value at which investors purchase fund shares from the fundcompany. The NAV value for a private entity may be derived by dividingthe total value of all the cash and securities owned by the privateentity, while accounting for any liabilities, and dividing that by anumber of an entity's outstanding shares. Therefore, the NAV for aprivate entity can be calculated based on the following formula:NAV=(assets−liabilities)/number of outstanding shares

For instance, the administrator of fund X values private entity A at$1.0 million where the private entity A has 10,000 outstanding shares.Therefore, the analytics server determines the NAV for private entity Ato be $100 per share.

As illustrated in table 1, fund X comprises shares associated withdifferent private entities where these shares are not equallydistributed among different private entities. For instance, privateentity A and B each comprises 25% of the overall value of fund X. Incontrast, private entity C comprises 50% of the value of fund X. Theseproportions may also be inputted by the administrator as part of theportfolio and transaction data. Accordingly, the analytics server mayuse fund X's attributes to determine a value for fund X. For instance,the analytics server may use the inputted values to generate an overallvalue for fund X. For instance, as depicted in Table 1, the analyticsserver determines that fund X has an overall value of $1.2 million. Asdescribed above, this value is based on the administrator's evaluationof the private entities within fund X. Therefore, $1.2 million is howthe administrator has valuated fund X. As described below, the analyticsserver may use this value as a benchmark to compare with the valuecalculated using the AI models.

The analytics server may generate a dataset comprising various datarecords where each data record corresponds to an attribute (inputted bythe administrator or calculated by the analytics server) of the fund.For instance, the analytics server may generate a dataset correspondingto the attributes illustrated in table 1. The analytics server may alsostore the dataset in an internal (e.g., local) or external electronicdata repository.

At step 220, the analytics server may execute a set of artificialintelligence models to identify a plurality of comparable publicentities to each private entity within the fund. The analytics servermay execute the set of AI models to identify one or more comparablepublic entities. As discussed below, the analytics server may usepublicly available data (e.g., financial data associated with publiclytraded companies, trading data) to identify the comparable publicentities and to predict a value for each private entity within the fund.

The set of AI models may include three separate models where each modelmay comprise a separate neural network trained via different algorithms.For example, a first AI model may comprise a first neural network thatutilizes a distance K nearest neighbor (DKNN) algorithm to clustervarious data points and identify one or more public entities that arecomparable to each private entity within the fund. The DKNN algorithm isfurther described in FIGS. 3A-C.

The set of AI models may also include a second AI model having a secondneural network that utilizes linear regression models to identify one ormore public entities that are comparable to each private entity withinthe fund. A linear regression model is a linear approach to modellingthe relationship between a scalar response (dependent variable) and oneor more explanatory variables (independent variables). In linearregression modeling, the relationships are modeled using linearpredictor functions whose unknown model parameters are estimated fromthe data. The second AI model may utilize a supervised machine-learningalgorithm to adapt to various data points and improve its efficiency andaccuracy. Supervised learning is the machine-learning task of learning afunction that maps an input to an output based on example input-outputpairs. A supervised learning algorithm analyzes the training data andproduces an inferred function, which can be used for mapping newexamples.

The analytics server may generate the second AI model using historicaldata associated with public entities. The analytics server may alsotrain the second AI model using supervised linear regression machinelearning algorithms. The analytics server may first create a neuralnetwork where each node represents historical financial information(e.g., stock prices and/or bond prices) of a publicly traded entity.Subsequently, the analytics server may train the model to predict andidentify a comparable public entity when attributes of a private entityis inputted. For instance, the analytics server may retrieve variousattributes associated with a private entity and the second AI model mayidentify/predict a comparable public entity. A public entity, as usedherein, refers to any publicly traded company or a company with publiclyaccessible financial status/reports.

The set of AI models may also include a third neural network utilizing aboosting tree regression model to identify one or more public entitiesthat are comparable to each private entity within the fund. Gradientboosting is a machine learning technique for regression andclassification problems, which produces a prediction model in the formof decision trees. For instance, the analytics server may build binarytrees by partitioning the data into two samples at each split node whereeach node represents a data point. Using the tree (e.g., traversing thetree) the analytics server may predict an outcome when presented withinput data.

Similar to the other AI models within the set of AI models, theanalytics server may generate the third AI model based on historicalfinancial information associated with publicly traded entities. Theanalytics server may train the third AI model using a Gradient BoostingAlgorithm. When the analytics server successfully trains the third AImodel, the analytics server may input attributes of a private entityinto the third AI model where the third AI model identifies/predicts acomparable public entity.

The analytics server may simultaneously execute all three models whereeach model may predict one or more comparable public entities. Theanalytics server may determine one or more comparable public entitiesbased on the output of each model. For instance, the analytics servermay consider all public entities identified by each model. In someembodiments, the analytic server may only consider an entity as acomparable public entity if a predetermined number of AI models (e.g.,two or all three of the AI models) have identified the public entity asa comparable public entity. For instance, for an entity to be consideredas a comparable public entity, the entity must be identified by at leasttwo of the three AI models.

In a non-limiting example, the analytics server may retrieve variousattributes of private entity A and may execute all three AI models toidentify/predict comparable public entities. Upon executing the set ofAI models, each AI model predicts the following public entities, asillustrated in table 2.

TABLE 2 DKNN Linear Regression Model Gradient Boosting Model PublicEntity - A/B/C Public Entity A/C Public Entity B/C/D

As discussed above, when different AI models predict different publicentities, the analytic server may either consider all public entitiesidentified by all AI models (A, B, C, and D), public entities commonamong all three AI models (public entity A), or public entitiesidentified by at least two models (public entity A and C). Uponidentifying comparable public entities, the analytics server may updatethe funds dataset accordingly.

In some configurations, the analytics server may sequentially executethe evaluation methods described above to determine a public entityclosely resembling a private entity. In some other configurations, theanalytics server may only use a selected number of models (e.g., twomodels).

At step 230, the analytics server may continuously retrieve financialtransaction data of the identified comparable public entities in realtime. The analytics server may retrieve the dataset including the listof all identified comparable public entities associated with the fund.Subsequently, the analytics server may retrieve financial transactiondata associated with the identified comparable public entities fromvarious data sources. For instance, the analytics server maycontinuously scan one or more electronic data repositories to retrieve areal time stock price of each comparable public entity.

In some configurations, the analytics server may retrieve comparablepublic entity data utilizing one or more application programming (API)interfaces in communication with one or more electronic sources. Forinstance, the analytics server may receive a notification from one ormore APIS when new and relevant data is identified. In a non-limitingexample, an API may transmit a notification to the analytics server whenthe market share price on a specific trading platform for apredetermined publicly traded company (public entity) has changed. Theanalytics server may program an API to continuously scan financial data(e.g., stock tickers, commodities, and bonds) associated with theidentified comparable public entities from various electronic sources.For instance, the API may continuously scan financial data in one ormore electronic marketplaces (e.g., NASDAQ and/or New York StockExchange) or other third-party index providers (e.g., Dow Jones and/orS&P). When the API discovers a change in the market price, the API maytransmit a notification including the new market price to the analyticsserver.

By allocating searching and notifying to multiple APIs, the analyticsserver may reduce the processing power needed by a single server.Furthermore, the trigger-based scanning allows the analytics server toprovide timely and efficient data to consumers.

At step 240, the analytics server may determine a value for each privateentity based on its respective comparable public entities. As discussedabove, the analytics server uses multiple AI models to identify one ormore comparable public entities. In some embodiments, the analyticsserver may consider data retrieved in step 230 (e.g., values of eachidentified comparable public entity) to determine a value for eachprivate entity within the fund. The analytic server may weight oraverage the values of the identified public entities to calculate thevalue of the private entity. For example, as discussed above, theanalytic server may identify public entities A and C as comparablepublic entities to private entity A within fund X. The analytics servermay also retrieve all financial transaction data associated with publicentity A and public entity C and may determine that public entity A isvalued at $1.2 million while public entities A is valued at $1.0million. The analytics server may average the above described values anddetermine that private entity A has a value of $1.1 million.

In some other embodiments, the analytics server may monitor theidentified comparable public entities and adjust the value of theprivate entity accordingly. For instance, when the values of two publicentities that are comparable to private entity A (public entity A and C)decrease by 15%, the analytics server may also decrease the value of theprivate entity A. The analytics server may decrease the value of privateentity A by 15% or by a predetermined factor/weight (e.g., 0.5×15%).

The analytics server may also determine an updated value for the fundbased on the newly calculated value of each private entity and the funddata received in step 210. For instance, when calculating a value forfund X, the analytics server may first calculate a value for privateentities A-C and then evaluate the fund X based on each private entity'sproportion within fund X, as described in table 3:

TABLE 3 Number Price of Per AI New price Private Fund's Shares OriginalPrivate model's New Per Private Entity Ratio Valuation Owned NAV EntityValuation NAV Entity A 25% $1.0M 1000 $100 $300,000 $1.1M $110 $330,000B 25% $1.5M 5000 $60 $300,000 $1.5M $60 $400,000 C 50% $12M 250 $2400$600,000 $12M $2400 $600,000 Total $1.2M $1.23M Value

As illustrated above, the analytics server executes the set of AI modelsand determines that private entity A is valued at $1.1 million.Accordingly, the analytics server calculates a new NAV value for privateentity A ($110). The analytics server also determines that fund X'sequity in private entity A is valued at $330,000 which is slightly abovethe previously calculated value by the administrator of the fund X($300,000). As a result, the analytics server determines a new value forFund X ($1.23 M).

At step 250, the analytics server may display the value of the fundbased on the valuation of each private entity where the identity of theprivate entities are anonymized. As described above, the analyticsserver may calculate a new value for the funds based on the newpredicted valuations of the private entities within the funds. Theanalytics server may populate a graphical user interface that displaysthe updated fund value. In some embodiments, the analytics server mayanonymize one or more private entities. As a result, the analyticsserver may display a new value for a fund without displaying whichprivate entity within the fund has an increased value.

In some configurations, the analytics server may not display theproprietary fund portfolio information inputted by the administrator(step 210). As a result, when users who do not have permission to viewsuch proprietary information log in the web-based application, they mayonly see an overall valuation of the fund without being able to view anyinformation associated with the underlying private entities. Forinstance, general users may not be able to view the name of the privateentities within a fund or how many shares of the private entities areowned by the fund. General users may also not be able to view theproportions associated with each private entity or the identifiedcomparable public entities.

In order to anonymize the value of a private entity, the analyticsserver may aggregate or disaggregate one or more private entities andtheir corresponding valuations. For instance, the analytics server mayaggregate the valuations for multiple private entities within a closedend fund. In this way, the analytics server ensures that a viewer cannotreverse engineer the overall valuation of a closed end fund to identifyvaluation of a single private entity. In a non-limiting example, theanalytics server may determine that a closed fund contains shares forten private entities. To anonymize the valuation of one or more privateentities within the list of ten private entities, the analytics servermay only provide a NAV for the closed end fund as a whole that does notinclude an itemized valuations.

In some configurations, the analytics server may delay the valuationprocess to prevent reverse engineering of a valuation. For instance, andcontinuing with the closed end fund example described above, when one ofthe ten private entities has had a publicly available event affectingits valuation, the analytics server may delay its valuation of theclosed end fund by a predetermined amount of time.

In some configurations, the analytics server may provide a disaggregatedvaluation that identifies a category associated with valuation ofdifferent private entities. For instance, and continuing with the closedend fund example described above, the analytics server may provide avaluation for all private entities (within the list of ten privateentities) that fall within “technology” or “manufacturing” categories.By unmasking the valuation of different categories of various privateentities, the analytics server may provide better insights to userswithout necessarily displaying individual valuation of the privateentities. In some configurations, the analytics server may customize thevaluation display based on instructions received from end users.

Referring now to FIG. 5, a graphical user interface of the AI supportedvaluation system is illustrated, in accordance with an embodiment. InFIG. 5, a user logs in a web-based application provided by the analyticsserver to view GUI 500. The analytics server may customize the GUI basedon user preferences and/or permissions. For instance, a user may nothave permission to view the names of private entities contained within afund and/or other features displayed within the GUI 500. In thoseembodiments, the analytics server may customize the GUI 500 so that theuser in unable to view content for which the user does not have viewingpermission.

The GUI 500 displays data related to four buyout funds, as illustratedin component 510. A buyout fund may refer to the process of obtainingcapital to buy companies (e.g., public and/or private entities) or toacquire other assets (e.g., stocks and bonds). Buyout funds aregenerally a type of private equity funds. Therefore, many users may beinterested in investing into a buyout fund based on its value. The GUI500 allows users to view a real time valuation of the buyout fundincluding detailed anonymized information regarding the private entitieswithin each fund of the buyout fund.

When the user interacts (e.g. clicks) on any of the buyout funds, theanalytics server limits the display to data associated with the selectedbuyout fund. For instance, as illustrated in GUI 500, the user hasinteracted with Barings 2019 P E Buyout fund. As a result, the analyticsserver displays a NAV for the selected buyout fund. The analytics servermay also display detailed information regarding the selected buyout fundin components 520-530. For instance, the selected buyout fund includestwo funds (Boston Capital Partners and Springfield Capital Partners)where each fund may include an indicator 521 and 522 respectively. Whenthe user interacts with the indicators 521 and/or 522, the analyticsserver may display anonymized detailed data regarding the privateentities included in each fund.

For instance, as depicted in GUI 500, the user interacts with indicator521. Consequently, the analytics server displays the component 530,which includes detailed information regarding the composition of theBoston Capital Partners fund. As described in component 530, BostonCapital Fund includes seven private companies (illustrated in column531). As depicted, the analytics server may anonymize the privateentities within Boston Capital Partners fund. As a result, the analyticsserver may display the overall NAV of the selected buyout fund and/orthe selected fund without divulging specific information regarding eachprivate entity.

The analytics server may also display NAV of each anonymized privateentity (column 532), fair valuation of each private entity as determinedby the selected fund administrator (column 533), and a value associatedwith each private entity's earnings before interest, taxes,depreciation, and amortization (EBITDA) in column 534. The purpose ofthe deductions in EBIDTA values is to remove the factors that businessowners have discretion over such as debt financing, capital structure,methods of depreciation. In some embodiments, the EBIDTA value isinputted by an administrator of the selected fund.

The GUI 500 may also include a column 535 where the analytics server canprovide different notifications and important details relevant to eachanonymized private entity. For instance, the analytics server maydisplay the indicator 536 indicating that the analytics server hasidentified new information regarding private entity 123. When the userinteracts with indicator 536, the analytics server may display moredetailed information regarding private entity 123.

In some embodiments, the analytics server may scan/crawl variouspublicly available data to identify material events to one or moreprivate entities. The material events may be any event that could affectthe valuation of a private entity. Non-limiting examples of a materialevents may include loss of key executive, addition of key executive,data breach, fraud, addition of key client, loss of key client,important regulatory changes, key geopolitical risk, material lawsuitreserve, natural disaster, and the like.

Using the GUI 500, the user may easily compare NAV calculated by theanalytics server for a fund with the fair value estimated by theadministrator of the fund. Additionally, the analytics server candisplay the NAV of a fund without divulging proprietary portfolioinformation. For instance, the GUI 500 may indicate that private entity123 may have a lower value than anticipated by the administrator ofBoston Capital Partners without indicating the identify of entity 123, acomparable public company to entity 123, or how many shares of entity123 is owned by Boston Capital Partners.

The GUI 500 may be displayed for end users, where the end users caninteract with various features and/or import the data directly intoother software applications. For instance, data displayed in the GUI 500can be populated in a home grown software where one or more graphicalcomponents are populated with data while preserving the look and feel ofthe homegrown software.

Additionally or alternatively, the GUI 500 may provide an option for theend users to approve, deny, or revise any of the predicted values. Forinstance, an end user (e.g., a close end fund manager) may review theNAV values and approve or deny the valuation generated by the analyticsserver. The end user may also revise the valuation generated by theanalytics server. When the analytics server receives the valuation fromthe end user, the analytics server may use the revised value to trainthe machine learning models described herein. The analytics server mayuser various training methods to train one or more models.

The training of the machine learning models may be holistic or granular.For instance, the analytics server may train one or more models togenerate revised values for a particular user. For instance, theanalytics server may identify that a particular user always revises thevaluation of a company and reduces the valuation by 20%. As a result,the analytics server may train the model to generate a reduced valuationcorresponding to that particular user's preferences. However, theanalytics server may only display the revised valuation for thatparticular user and display a different valuation for other users.

Additionally or alternatively, the analytics server may holisticallytrain the machine learning algorithms and revise the models, such thatall users view the revised value. For instance, when a predeterminednumber of (or proportion of) end users revise a value, the analyticsserver may train the model accordingly, such that all users will see anew valuation based on the received revised values from the end users.

In some embodiments, the analytics server may provide real-time updatesof the above-mentioned valuations. Referring now to FIG. 6, a graphicaluser interface of the AI supported valuation system is illustrated, inaccordance with an embodiment. GUI 600 illustrates NAV of differentfunds where the NAV is calculated utilizing the AI-based valuationmethods disclosed herein. For instance, the analytics server maycontinuously perform the steps of method 200 and update NAV for one ormore funds in real time. Accordingly, the analytic server maygraphically represent NAV of one or more funds, as illustrated in GUI600.

For instance, line 601, 602, and 603 may each represent a fund whereeach data point within these lines represents the NAV for the respectivefund within a particular time. In the GUI 600, data point 604 representsthat at time t1, the fund represented by line 603 had a NAV of $60million. Using the real time updates and trends illustrated in GUI 600,a user may track NAV progress of a fund in real time. Even though GUI600 illustrates NAV of different funds, users may customize the GUI 600so that each line represents a customize value. As described above, theanalytics server may anonymize the private entities contained withineach fund so that proprietary data is not viewed by unauthorized users.For instance, the user viewing the GUI 600 may only view the NAV of thefunds resented by lines 601-603 without viewing any underlying data(e.g., number or identify of the private entities within the fund).

In some embodiments, the analytics server may also display andcontinuously update an indicator (e.g., ticker) corresponding to thevalue of one or more funds. A ticker may refer to a report of the pricefor certain fund, updated continuously. A “tick” is any change in price,whether that movement is up or down. As a result, the analytics servermay display real time NAV of one or more funds.

As described above, the AI model may account for all public informationand compare a private entity to one or more comparable publicly tradedcompanies. Therefore, in order to generate results in real time, theanalytics server may continuously and iteratively scan variousdatabases. In that way, the analytics server identifies the latestmarket movement and/or relevant data and iteratively and continuouslyupdates the AI model.

Referring now to FIG. 3, a flow diagram of a process executed in an AIsupported valuation system is illustrated, in accordance with anembodiment. The method 300 includes steps 300-330. However, otherembodiments may include additional or alternative execution steps, ormay omit one or more steps altogether. In addition, the method 300 isdescribed as being executed by a server, similar to the analytics serverdescribed in FIG. 1. However, steps of method 300 may also be executedby any number of computing devices operating in the distributedcomputing system described in FIG. 1. For instance, one or more usercomputing devices may locally perform part or all the steps described inFIG. 3.

The method 300 describes an improvement to the existing and conventionalartificial intelligence methods for clustering and predicting dependentvalues and variables. Therefore, the present disclosure first describesthe technical shortcomings of conventional clustering methods (e.g., Knearest neighbor or KNN) before describing method 300 in more detail.

As described in FIGS. 4A-4B, conventional AI supported clusteringmethods either produce inaccurate results or require high processingpower. Because both of the above-mentioned technical challenges arehighly undesirable in the technical field of artificial intelligence,there is a desire for a method and system to produce highly accurateresults without requiring high processing power. The method 300addresses the above-mentioned technical deficiencies of the conventionalAI models.

Referring now to FIG. 4A, an example of a conventional clustering methodis illustrated, in accordance with an embodiment. Graph 400 graphicallyrepresents a training dataset including various data points where eachdata point within graph 400 has an independent attribute/variable and acorresponding dependent attribute/variable. As illustrated in graph 400,each data point has an X attribute (independent value) and acorresponding Y value (dependent value). For instance, data point 431may have an independent X value (X1) and a corresponding dependent value(Y1).

The dataset depicted in FIG. 4A may be retrieved and generated based onhistorical data. Therefore, all the data points illustrated in graph 400(except data point 427) represent known and historical data Therefore,each data point may correspond to a different publicly traded entitywhere the X-axis corresponds to a cash flow value of a public entity andthe Y-axis corresponds to a NAV of the public entity. For instance, datapoint 431 may represent public entity A where X1 is a cash flow value ofthe public entity A and Y1 represents the value of public entity A.

FIGS. 4A-B illustrate how a conventional AI model clusters the datasetdepicted in graph 400 to predict Y values associated with an unknowndata point (e.g., data point 427). For instance, an AI model may betrained to receive X2 and predict Y2.

In some embodiments, conventional AI models may use various clusteringmethods (e.g. KNN) to generate multiple clusters within the trainingdataset. KNN is a non-parametric method used for classification andregression. For instance, conventional AI models may generate cluster410, cluster 420, and cluster 430. Also as depicted, the above-describedclusters may not include outlier data, such as data point 411, 421, and422. Conventional AI models first determine a cluster corresponding tothe independent attribute of the new/unknown data point. Subsequently,based on the dependent value of the known data points within theidentified cluster, conventional AI models may determine/predict adependent attribute for the new data point.

For example, when predicting a dependent attribute for data point 427(Y2), the AI model may first retrieve/determine an independent attributeof the data point 427 (X2). In some embodiments, the independentattribute (X2) may be inputted by a user. Based on the X2 value,conventional AI models determine that the data point 427 corresponds tocluster 420. This is because the X2 value of the data point 427 iswithin a predetermined threshold of the X value for other data pointswithin the cluster 420. Conventional AI models may use the dependentattributes of the data points within cluster 420 predict a dependentattribute of the data point 427 (Y2).

The conventional AI models use two methods to predict the dependentattribute of a new data point. First, the conventional AI modelscalculate the dependent attribute of the new data point by averaging thedependent attributes for the data points within the identified cluster.For instance, to identify the Y2 value, conventional AI model maydetermine an average for the Y values of data points 422-426. Thismethod may not produce highly accurate results. Second, the conventionalAI models may generate a weighing factor based on the data points withinthe identified cluster. In this method, the conventional AI model mayfirst calculate a pairwise distance for each data point within theidentified cluster, create a distance matrix, and generate a weightfactor to be applied to the independent attribute of the new data point.

Referring now to FIG. 4B, an example of a pairwise distance matrix isillustrated. As described above, in traditional KNN clustering methods,the AI model may calculate pairwise distance between data points withina cluster to improve the output accuracy. For instance, to calculate Y2,conventional AI models may be required to calculate a distance betweenall data points within cluster 420. The conventional AI models maygenerate a distance matrix, such as distance matrix 440. As depicted,each row and column may represent a particular data point within acluster and each cell may represent the distance between thecorresponding data point represented by its respective column and row.For instance, cell 441 represents the distance between data point 422and data point 425.

The conventional AI models may use a Euclidean algorithm to calculateeach pairwise distance. As a result, the conventional AI models arerequired to calculate n×(n−1) combination of distances where nrepresents a number of data points within a cluster. For instance,conventional AI models must calculate 20 pairwise distances for cluster420, which includes only five data points (422-426).

Upon calculating the distance matrix 440, the analytics server may alsobe required to calculate a weight factor for the predicted data point427. The weight factor may be dynamically calculated based on a distancebetween a known attribute of the data point 427 (X2) with other datapoints within the cluster 420 to predict the unknown attributeassociated with the data point 427 (Y2). For instance, the weight factorfor data point 425 may be higher than the weight factor of data point422 because data point 427 has a shorter distance to the data point 425than data point 422. As a result, the predicted attribute of the datapoint 427 (Y2) is closer to data point 425 than data point 422.

The described second method of predicting Y2 is time-consuming andrequires heavy processing power because this method requires the AImodel to execute a high number of distance calculations. Furthermore,averaging (first method) or weighting (second method) may still notproduce highly accurate results. Therefore, there is a need to produceaccurate predictions without consuming a high processing power.

Referring back to FIG. 3, at step 310, the analytics server may generatea dataset comprising a first set of independent data points and a secondset of data points dependent on the first set of data points. In orderto generate the dataset, the analytics server may retrieve historicaldata associated with multiple public entities (e.g., publicly tradedcompanies). The dataset may include two separate sets of data comprisingdifferent data points. The first set of data points may represent anindependent attribute/variable and the second set of data points mayrepresent another attribute that is dependent upon the independentattribute. The analytics server may use this dataset to generate and/ortrain an AI model.

In a non-limiting example, the dataset may include multiple independentattributes (e.g., cash flow, net income, assets, employee satisfaction)and a corresponding dependent attribute associated with the publicentity (e.g., value of the public entity). For instance, each data pointmay indicate a cash flow value (independent attribute) and a companyvalue (dependent attribute) of a public entity. In another example, eachdata point may include a value corresponding to total earnings of apublic entity (independent attribute) and a corresponding company value(dependent attribute). In some embodiments, the analytics server maysort the dataset into different groups consisting of uniform attributes.For instance, the dataset may be divided into subgroups (e.g., clusters)where data points within each subgroup share an independent anddependent attributes. For instance, the analytics server may sort thedataset into a subgroup corresponding to cash flow and another subgroupcorresponding to assets.

At step 320, the analytics server may generate a new training dataset.The analytics server may first execute a clustering or groupingalgorithm to identify one or more clusters or groups within the originaldataset. For instance, the analytics server may execute a KNN algorithmto identify one or more clusters within the dataset. Upon identifyingthe clusters, the analytics server may generate a new training datasetfor each cluster to train the AI model accordingly. In some embodiments,the analytics server may generate the new training dataset for theentire original dataset (step 210) and not limited to the data pointswithin the same cluster or group. The analytics server may generate twonew data points for each pair of original data points within a cluster.Therefore, the analytics server may generate a total number of newtraining data points (n²) for a cluster with n data points.

At step 330, the analytics server may train the AI model based on thenew training dataset. The analytic server may train the AI model usingany of the training techniques such as regression algorithms, GradingBoosting Regression algorithms, linear regression algorithms and deepneural networks. Upon training the AI model, the analytics server mayexecute the trained AI model to predict the distance between thedependent value of a data point within the original dataset and apredicted dependent value for new data point.

Referring now to FIG. 3B, a flow diagram of a process executed in an AIsupported valuation system is illustrated, in accordance with anembodiment. As illustrated in step 340, the analytics server maygenerate an original dataset where each data point within the originaldataset is represented by (X_(i), Y_(i)). As described above, theoriginal dataset comprises data points corresponding to an independentvariable (X_(i)) and a corresponding dependent variable (Y_(i)). In anon-limiting example, X_(i) may represent an independent attribute(e.g., cash flow, assets, volume of sales, employee satisfaction) of apublic entity and Y_(i) may represent a dependent value, such as thevalue (net asset value) of the public entity.

As illustrated in step 350, the analytic server may create a newtraining dataset for each group or cluster within the original dataset.As described above, the analytics server may first execute a clusteringalgorithm and identify one or more clusters corresponding to theoriginal dataset. The analytics server may create new training datapoints for each cluster. The analytics server may create two new samplesfor each pair of data points within the same cluster. For instance, fordata points i and j, the analytics server may create two new data points(X_(i), X_(j), Y_(i)−Y_(j)) and/or (X_(j), X_(i), Y_(j)−Y_(i)).

As illustrated in step 360, the analytic server may train the AI model(represented by function “f”) based on the new training datasets. Asdescribed above, the analytic server may use a variety of regression orother training methods to train the AI model. The analytics server maytrain the AI model to predict the distance between different data pointswithin a cluster. Therefore, in contrast with conventional methods ofaveraging the distance or weighing the distance between different datapoints within a cluster, the analytics server may predict a moreaccurate distance by training the model based on the distance itself.For instance, the analytics server may execute the trained model usingX_(new) to predict a dependent value (e.g., Y_(new)) by predicting thedistance between Y_(new) and other data points within a cluster (e.g.,group g). For instance, when the user inputs Xnew, the analytics serveridentifies that X_(new) belongs to group g, and predicts the distancebetween Y_(new) and other Y values within group g.

The trained AI model can calculate the pairwise distance between X_(new)and X_(r) where X_(r) represents each X value of the data points withinthe identified cluster. Ideally, the training model should be symmetricwith respect to i and j values. However, because the training model isnot guaranteed to be symmetric, the analytics server may use thefollowing formula as the predicted distance to preserve the symmetry(step 370):d _(k,r) =|f(X_f _(k,r))−f(X_f _(r,k))|/2

In some embodiments, the analytics server may determine multiplepredicted values (Y_(r)), sort the value, and select the top value (step371) or use the weighted average as prediction (step 372).

In a non-limiting example, the analytics server may receive a request tocalculate value of a company Z using methods and systems describedherein (e.g., FIG. 3B). The analytics server may determine the value ofcompany Z using three other companies 1-3, as illustrated below in Table4a:

TABLE 4a company X_1 (debt) X_2 (sales) Y (Value) Z 100 200 ? 1 60 15010 2 60 160 20 3 110 200 15

As illustrated above, the analytics server may use two independentvariables (X_1 representing debt associated with each company and X_2representing sales of each company). As illustrated in step 350, theanalytics server may generate new data points as illustrated in Table4b:

TABLE 4b Company pair X_(i), X_(j) Y_(i)-Y_(j) comp1, comp2[60,150,60,160] −10 comp2, comp1 [60,160,60,150] 10 comp1, comp3[60,150,110,200] −5 comp3, comp1 [110,200,60,150] 5 comp2, comp3[60,160,110,200] 5 comp3, comp2 [110,200,60,160] −5

The analytics server may train the artificial intelligence models usingthe newly generated data points. The analytics server may then createthe following table that uses known values of companies 1-3 and companyZ, as illustrated in Table 4c:

TABLE 4c Company pair X_(i), X_(j) Y_(i)-Y_(j) comp1, compZ[60,150,100,200] ? compZ, comp1 [100,200,60,150] ? comp2, compZ[60,150,100,200] ? compZ, comp2 [100,200,60,150] ? comp3, compZ[110,200,100,200] ? compZ, comp3 [100,200,110,200] ?

The analytics server may use the model trained in step 360 to predictthe Y_(i)−Y_(j) in Table 4c and calculates the following values,illustrated in Table 4d:

TABLE 4d Company pair Y_(i)-Y_(j) comp1, compZ 1 compZ, comp1 −1 comp2,compZ 2 compZ, comp2 −3 comp3, compZ 3 compZ, comp3 0

The analytics server then calculates the table below illustrating thedistances of various data points:

TABLE 4e Company pair Y_(i)-Y_(j) d_z,1 [1−(−1)/2=1 d_z,2 (2+3)/2=2.5d_z,3 (3+0)/2=1.5

As illustrated in Table 4e, company 1 is closest to company Z withcompany 3 and then company 2 following. The analytics server may sortthe companies based on their respective distance value (i.e., company 1,company 3, and company 2). In some configurations, the analytics servermay average the values for company 1 and 3 using equal weights toestimate a value for company Z, as illustrated below:(y_1+y_3)/2=(10+15)/2−12.5

Therefore, the analytics server calculates the value for company Z to be12.5. In some configurations, the analytics server may weight theresults based on each respective company's distance to company Z.

Using the method described in FIG. 3B, the analytics server may identifythe independent variables that give rise to a dependent variables thatmay be clustered together. For instance, when company 1 and company 2are in a cluster (e.g., both companies have a dependent variable (i.e.,valuation) that are reasonably similar), the analytics server mayidentify what independent variable corresponding to these companies arereasonably similar that caused these companies to be clustered together.As a result, the analytics server may identify counter intuitiverelationships for companies that may or may not be similar. Forinstance, that two dissimilar companies have both have an independentvariable that are reasonably close to the valuation of each company.Therefore, the analytics server can predict the first company'svaluation using the second company's characteristics.

Using the valuation approach described herein, the analytics server mayaccurately value a portfolio of public companies using approximately 14accounting variables taken from publicly released quarterly financialstatements and the daily stock prices of comparable firms. Due to usingthe machine-learning and other algorithms described herein, thevaluation is reasonably accurate. The analytics server may iterativelytrain the model(s) described herein over time and throughout variouseconomic cycles to improve the valuation's accuracy. Using the methodsand systems described herein, the analytics server may value publicequities using a quantitative approach, allowing market participants tovalue private equities (accounting variables taken from privatecompanies, which are anonymized and published on an electronic platformprovided by the analytics server and daily stock prices of comparablepublic firms).

FIG. 4C illustrates the connection between financial transparency andvaluation in both the public and private markets, according to anembodiment. FIG. 4C also compares how companies (either private orpublic) are often valued using conventional approaches termed “multiplesanalysis” and how the methods and systems described herein improve theseconventional solutions. The analytics server may use various moresophisticated market participant techniques, such as regressionanalysis, to refine the results produced by multiples analysis. FIG. 4Calso illustrates how the analytics server utilizes machine learning andthe logic of “multiples analysis” and the advantage of “big dataanalytics” to enhance regression approaches utilized by conventionalsoftware solutions.

Conventionally, investment practitioners frequently use comparablecompanies to value a company that they are considering buying orselling. The comparable company method is very similar to using thesales prices from homes in your neighborhood to determine the value of ahouse. Real estate appraisers find comparable homes, calculate anaverage value, and then make adjustments to account for your home'sspecific characteristics.

Comparable company valuation is also referred to as “multiples”analysis. For instance, assume the following is true for ABC Company, ahypothetical example illustrated in Table 5. The value of ABC Company'sstock price multiplied by the number of shares outstanding is worth $425million. This value is the market capitalization of ABC Company.Analysts will then review financial statements for metrics that areassociated with market value. One common metric is Earnings, beforeInterest, Taxes, Depreciation, and Amortization (EBITDA). ABC Company'sEBITDA, over the last twelve months, was $40 million. If an analyst thendivides ABC Company's market capitalization by its EBITDA, the analystcalculates a “multiple” of 10.6. If that analyst then wanted to quicklyestimate a value for a second company, which does not have a marketcapitalization, XYZ Corp., the analyst could multiply XYZ Corp's EBITDAof $12 million times the EBITDA Multiple of 10.6 to estimate the publicmarket value of $127.2 M.

TABLE 5 ABC Company, (trailing twelve month finoncial data) MarketCapitalization $425 M Earnines before interest, Taxes. Depreciation, and$40 M Amortization (EBITDA) EBITDA Multiple 10.6 XYZ Company, (trollingtwelve month financial data) Market Value Unknown EBITDA $12 M AssumedEBITDA multiple from ABC Company 10.6 Implied Value $127.2 M

In this simple example, the analyst estimates the value of XYZ byfinding a comparable company, calculating the EBITDA Multiple, and thenmultiplying the EBITDA multiple times XYZ's EBITDA. As illustrated, avaluation of $127 M in not an accurate valuation of this company.However, the EBITDA multiple may be reliable enough that it is widelyused by practitioners, especially when triangulating potential marketvalues of a private company.

In addition to the above-described technical advantages, the methods andsystems described herein may improve the accuracy of conventionalvaluation processes. Statistical approaches, such as regression modelsor machine learning, may improve multiples analysis. In the exampleillustrated in Table 5 above, the calculation of the value of XYZ Corp.used only one comparable company, ABC Company, and one ratio: ABCCompany's Market Capitalization to EBITDA. Disclosed methods and systemutilized by the analytics server may more accurately estimate XYZ Corp'svalue in two ways. First, the analytics server may include more ratiosin the analysis. For instance, the analytics server may compare ABCCorp's Market Capitalization to Revenue. Second, the analytics servermay include additional publically traded comparable companies in theanalysis. Therefore, instead of solely relying on ABC Company's EBITDAMultiple, the analytics server may calculate the EBITDA multiple ofother publically traded comparable companies and then utilize theaverage or median EBITDA multiple of the comparable company set.

If an analyst is estimating the value of portfolio of companies, ratherthan a single company like XYZ Corp., then the analytics server mayutilize a third method to increase the accuracy of basic “multiplesvaluation.” In some configurations, the analytics server may increasethe diversity of the portfolio, increasing the number of companies andthe number of different kinds of companies. As expected, in anystatistical method, there may be estimation error (e.g., noise).However, when the analytics server increases the diversity of theportfolio, given certain assumptions, the “noise” may be reduced.Therefore, if the analytics server has an average error of plus or minus10% when estimating the value of a single company, the error associatedwith estimating the entire portfolio can be much lower than 10%. Thismay be due to the analytics server reducing the error margins byaggregating some overestimates with some underestimates.

Frequently, private equity (PE) valuation is time consuming, tedious,and produces inaccurate results because a broad set of variables impactthe future cash flows of a company. The interactions between variables,and how they change over time, is difficult for analysts to detect. Thisdifficulty is compounded by the voluminous amounts of economic dataavailable for analysis. Moreover, conventional valuation methods arehighly dependent upon each analyst's subject understanding of differentvariables, which produces unreliable and inconsistent results.

Machine learning algorithms (e.g., valuation methods or trainingmethods) described herein) are designed for situations where the amountof data is large, and the interactions between the data are complex,which is nearly impossible for a human analysist to compute usingconventional software solutions or other methods.

Unlike other machine learning approaches, which can be a “black box,”the techniques disclosed herein improve conventional machine learningand AI techniques used. For instance, many conventional AI-enabledtechniques use KNN methodologies. KNN is a method for classification andregression of data. In an example, one can envision a KNN model creatinga plot chart. KNN models can plot points that are close to each other ona graph (i.e., neighbors) and identify clusters of data. In anillustrative example, there may be six companies in a dataset, includingthe “target company.” Conventional software solutions utilizing KNN maybe utilized to identify a nearest neighbor for the target company. Asillustrated, the dataset may include data corresponding to sales andEBITDA for each company. Conventional software solution could plot eachcompany on a chart, placing sales on the X-axis and EBITDA on theY-Axis. Using KNN, conventional software solutions would then computethe distance between each plot point. As illustrated in FIG. 4C, CompanyD has the closest distance to the target company and is, therefore, thenearest neighbor.

The methods and systems described herein enhance the standard andconventional KNN models. Simply put, the analytics server may not try tofind comparable companies with the smallest distance between sales andEBITDA, but the smallest “distance” between drivers of economic value(e.g., data points). The economic value of private companies, however,is unknown, except at certain discrete points in time. Market prices forprivate firms are only available when the equity in the firm is boughtor sold, often in private, unobservable transactions. In the normalcourse of running a PE fund, private companies are purchased in thefirst several years of the fund and then sold five to seven years later,before the fund closes and returns capital and any profit to investors.In between those points, valuations conducted by the management of theprivate company or PE fund sponsors are only estimates.

To value the target company on a daily basis, the analytics server canadapt the guideline public company and traditional KNN machine learningapproaches by conceptually following five steps:

First, the analytics server may estimate the value of the “targetcompany” by comparing its economic sector and accounting data to theeconomic sector, accounting data, and market price of publicly tradedfirms. For this step, the analytics server may use a machine learning,regression-based model broadly referred to as the “Gradient BoostingTree” method. Once this step is complete, the analytics server canestimate the market value, enterprise value, or other metrics for thetarget company.

Second, the analytics server may compute estimated “multiples” (or someother measure of economic value) for the target company. For instance,the analytics server may divide the estimated enterprise value (EV) ofthe target company by its actual EBITDA to get an estimated EV/EBITDAratio.

Third, the analytics server may measure the distance between theestimated EV/EBITDA ratio of the Target Company versus the actualEV/EBITDA ratio of all public companies in the same sector. The publiccompany with the smallest distance between estimated and actualEV/EBITDA ratio would be considered the “nearest neighbor.” The approachmay be broadly referred to as DKNN. Furthermore, the analytics servercan compare the distance of one estimated metric versus one actualmetric to find the nearest neighbor (as we illustrated above) or maycalculate the distance between multiple metrics and weight each one.

The number of metrics utilized in DKNN can change over time or bysector. While the analytics server utilizing the machine learning and AIalgorithms can recommend which metrics to use, in some configurations,valuation and finance experts can select the metrics recommended by theanalytics server and determine the ideal number of metrics to use-oftenfewer than five. Manual changes to the model will be governed by agovernance framework. Simply put, machine learning may augment/improveand not automate valuation of complex assets.

Fourth, the analytics server may determine the number of nearestneighbors to include in the comparison set for the target company. Themachine learning models utilized by the analytics server may dynamically“learn” the optimal number of neighbors by using machine learningapproaches.

Fifth, at this point, the analytics server may have estimated the valueof the target company using a regression model. The analytics server mayalso have identified comparable public companies for the target companyusing the DKNN approach. The comparable companies may be all publiccompanies that are valued by the market on a similar economic basis. Theanalytics server may then track the changes in market value of thecomparable companies and apply the same changes to the estimated valueof the target company. Thus, the analytics server can estimate a publicmarket equivalent price for any equity, public or private, using machinelearning to implement an industry standard multiples analysis approach.

FIG. 4D illustrates how the valuation method described above is testedon a portfolio of public equities, in accordance with an embodiment.Chart 460 illustrates an experiment to randomly create a portfolio of100 public companies and to estimate the value of all 100 publiccompanies to compare the total estimated market capitalization to thetotal actual market capitalization for the same 100 public companies.

For the illustrated experiment, the analytics server utilized a GradientTree Boosting method to estimate the market capitalization of a targetcompany and a simplified version of the DKNN model, detailed above, wasalso used to find comparable public companies. The analytics serverutilized 14 financial statement data points and market capitalization asinputs to train the Gradient Tree Boosting model. The metrics selectedare frequently used by practitioners for valuation and available frommost private equity firms to which we have spoken. Table 6 provides alist of the metrics used in the model as well as their relativeimportance during different business cycles.

The Gradient Tree Boosting model may be “retrained” quarterly or basedon any other frequency. As market participants weight certain financialstatement metrics differently during changing economic cycles, theanalytics server may trains the models accordingly. For instance,pre-crisis of 2008, the level of sales was an important considerationfor analysts but during the crisis, analysts placed more emphasis on netincome. These changes make intuitive sense: before the crisis, analystswere optimistic and more concerned with a company's absolute sales.During the crisis, analysts were more pessimistic, and were ultimatelyworried about a firm's survival, and therefore were more concerned aboutprofit. Overall, however, the five most important accounting metricswere relatively consistent throughout the test period.

TABLE 6 VARIABLE IMPORTANCE SCORE BEFORE DURRING AFTER VARIABLE CRISISCRISIS CRISIS EBITDA 0.20 0.23 0.24 Net Income 0.15 0.23 0.21 CAPEX 0.140.11 0.14 Total Debt 0.13 0.12 0.09 Sales 0.12 0.09 0.11 EBITDA Margin0.04 0.06 0.04 Debt to EBITDA 0.04 0.02 0.05 Operating EBIT 0.04 0.030.05 Debt to Equilty 0.03 0.03 0.02 Annual Dividend Payout 0.03 0.010.02 (% Earnings) - Total Dollar Return on (average) assests 0.02 0.010.01 Interest Expense 0.02 0.03 0.02 Return on (average total) equity0.02 0.02 0.01 Sales Growth 0.01 0.01 0.01

The values in Table 6 may represent the average variable importancescore during the corresponding business cycle. The larger the value, thegreater the importance during that business cycle. Importance may changeover time. For example, CAPEX is more important than total debt beforethe crisis, but not during the crisis. Utilizing the methods and systemsdescribed herein the analytics server may be able to estimate the totalvalue of a 100 company portfolio with less than 5% prediction error onaverage. Line 461 is the actual value of the companies and line 462represents the valuation calculated using the methods and systemsdescribed herein.

FIG. 7 illustrates a graphical user interface generated by the analyticsserver, in accordance with an embodiment. GUI 700 illustrates a web pagethat may be viewed by a closed end fund manager or other end users. GUI700 may include a graphical component 702 that illustrates a daily NAVfor private entity closed end fund (PE CEF). Each day, the PE CEFs canpost an independently estimated NAV on an electronic platform (e.g.,website) provided by the analytics server. Investors can view detailsabout the estimated NAV at the PE CEF, PE Fund, and portfolio companylevels. If investors click on (or otherwise interact with) a section ofthe pie-chart displayed in the graphical component 702, the analyticsserver may display the estimated NAV by economic sector for privateequity investments as well as the percentage of holdings held in otherinvestments, such as cash, treasuries, and exchange-traded funds.Investors can further evaluate the NAV by clicking “PE Fund Details” or“Portfolio Company Details” illustrated in GUI 700.

GUI 700 may also include daily updates of portfolio allocation. Ascapital calls are made by PE funds, or distributions are received, theNAV of each PE CEF will change. The analytics server may display thisinformation to investors daily. GUI 700 may also include performancehistory of various PEs within the CEF. Each PE CEF may have charts andtables that display the fund's performance history over differentperiods of time and compared to appropriate indexes. The GUI 700 mayalso include top ten holdings of the CEF. The analytics server maydisplay the top ten holdings of underlying portfolio companies. Topholdings may be the private equity companies with the greatest marketvalue weight in the closed end fund portfolio. Top holdings may bedetermined by the market value they comprehensively represent within thetotal portfolio.

FIG. 8 illustrates a graphical user interface generated by the analyticsserver, in accordance with an embodiment. GUI 800 illustrates a web pagethat may be viewed by a closed end fund manager or other end users. GUI800 illustrates private equity fund details. In an embodiment, once anend user reviews the summary “tab” of BGF, the end user may click on (orotherwise interact with) the “Private equity fund details” graphicalbutton to learn more about the private equity funds in which BGF hasinvestments. The analytics server may then display an estimated NAV foreach PE fund, such as Boston Capital Partners and Springfield Holdings(hypothetical PE funds illustrated herein). The analytics server may usethe methods described herein to estimate the value of each underlyingportfolio company and then aggregates the value of each portfoliocompany. The GUI 800 may also include performance metrics for each PEfund. As an example, for Boston Capital Partners, GUI 800 may displaythe internal rate of return to date, the amount of capital committed,funded, and distributed, as well as other helpful data.

GUI 800 may anonymize the names of each portfolio company so that eachcompany can continue to operate with the benefits of privateownership-benefits that may be passed on to retail investors in the formof anticipated premium over the S&P 500 index. Each private equitycompany may be tracked by the analytics server using an assigned number.For each company, the analytics server may display the market sector,independent estimate of NAV, and select financial statement data.

GUI 800 may also include financial statements details shared withInvesting public. All investors can access select financial statementinformation from each portfolio company's financial statements (e.g.,revenue and EBITDA) a feature that allows investors to value eachportfolio company on “multiples” basis. The analytics server may collectthe financial statement information from private equity funds on theirunderlying portfolio companies quarterly and maintains a history of thefinancials within our database.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of this disclosure orthe claims.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the claimedfeatures or this disclosure. Thus, the operation and behavior of thesystems and methods were described without reference to the specificsoftware code being understood that software and control hardware can bedesigned to implement the systems and methods based on the descriptionherein.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed herein may be embodied in a processor-executable softwaremodule, which may reside on a computer-readable or processor-readablestorage medium. A non-transitory computer-readable or processor-readablemedia includes both computer storage media and tangible storage mediathat facilitate transfer of a computer program from one place toanother. A non-transitory processor-readable storage media may be anyavailable media that may be accessed by a computer. By way of example,and not limitation, such non-transitory processor-readable media maycomprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othertangible storage medium that may be used to store desired program codein the form of instructions or data structures and that may be accessedby a computer or processor. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and Blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media. Additionally, the operations of a method oralgorithm may reside as one or any combination or set of codes and/orinstructions on a non-transitory processor-readable medium and/orcomputer-readable medium, which may be incorporated into a computerprogram product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the embodimentsdescribed herein and variations thereof. Various modifications to theseembodiments will be readily apparent to those skilled in the art, andthe principles defined herein may be applied to other embodimentswithout departing from the spirit or scope of the subject matterdisclosed herein. Thus, the present disclosure is not intended to belimited to the embodiments shown herein but is to be accorded the widestscope consistent with the following claims and the principles and novelfeatures disclosed herein.

While various aspects and embodiments have been disclosed, other aspectsand embodiments are contemplated. The various aspects and embodimentsdisclosed are for purposes of illustration and are not intended to belimiting, with the true scope and spirit being indicated by thefollowing claims.

What is claimed is:
 1. A method comprising: receiving, by a server,transaction data associated with a fund comprising a plurality ofprivate entities, the transaction data corresponding to a proportion ofthe fund associated with each private entity, shares purchased of eachprivate entity, and purchase price for the shares purchased of eachprivate entity; executing, by a server, a set of artificial intelligencemodels to identify a plurality of comparable public entities to eachprivate entity, the set of artificial intelligence models comprising atleast a first artificial intelligence model utilizing a learned distancek-nearest algorithm to identify the plurality of comparable publicentities, a second artificial intelligence model utilizing a linearregression algorithm to identify the plurality of comparable publicentities, and a third artificial intelligence model utilizing a boostingtree regression algorithm to identify the plurality of comparable publicentities, wherein the set of artificial intelligence models are trainedin accordance with a training dataset comprising known attributes of aset of public entities and a corresponding valuation for each publicentity within the set of public entities; retrieving, by the server,financial data associated with the identified comparable publicentities; determining, by the server, a value for each private entitybased upon its respective identified plurality of comparable publicentities; and displaying, by the server on a graphical user interface inreal time, an indicator of a value of the fund, the graphical userinterface comprising a value of each private entity within the fundwhere an identify of each private entity is anonymized.
 2. The method ofclaim 1, wherein the server dynamically updates the indicator in realtime in accordance with modifications in financial data associated withthe identified comparable public entities.
 3. The method of claim 2,wherein the graphical user interface displays a trend associated changesassociated with the updated indicator for a predetermined time.
 4. Themethod of claim 1, wherein the identified comparable public entities arealso displayed on the graphical user interface.
 5. The method of claim1, wherein an application programming interface protocol notifies theserver when any financial data of an identified comparable public entityhas been modified.
 6. The method of claim 1, further comprising:crawling, by the server, one or more databases to identify one or moreweb documents associated with at least one private entity; anddisplaying, by the server, an indicator on the graphical user interfaceindicating identification of at least one web document associated withat least one private entity.
 7. The method of claim 1, wherein thegraphical user interface further displays a value of each private entitywithin the fund.
 8. The method of claim 1, wherein the graphical userinterface further displays a second value of the fund received from auser computing device.
 9. The method of claim 3, wherein the period oftime is customizable by a user interacting with the graphical userinterface.
 10. The method of claim 9, wherein the server retrieved thefinancial data in real time.
 11. A computer system comprising: one ormore electronic data sources configured to store financial dataassociated with a set of public entities; and a server connected to theone or more electronic data sources, the server having at least oneprocessor configured to: receive transaction data associated with a fundcomprising a plurality of private entities, the transaction datacorresponding to a proportion of the fund associated with each privateentity, shares purchased of each private entity, and purchase price forthe shares purchased of each private entity; execute a set of artificialintelligence models to identify a plurality of comparable publicentities to each private entity, the set of artificial intelligencemodels comprising at least a first artificial intelligence modelutilizing a learned distance k-nearest algorithm to identify theplurality of comparable public entities, a second artificialintelligence model utilizing a linear regression algorithm to identifythe plurality of comparable public entities, and a third artificialintelligence model utilizing a boosting tree regression algorithm toidentify the plurality of comparable public entities, wherein the set ofartificial intelligence models are trained in accordance with a trainingdataset comprising known attributes of the set of public entities and acorresponding valuation for each public entity within the set of publicentities; retrieve, from the one or more electronic data sources,financial data associated with the identified comparable publicentities; determine a value for each private entity based upon itsrespective identified plurality of comparable public entities; anddisplay, on a graphical user interface in real time, an indicator of avalue of the fund, the graphical user interface comprising a value ofeach private entity within the fund where an identify of each privateentity is anonymized.
 12. The computer system of claim 11, wherein theserver dynamically updates the indicator in real time in accordance withmodifications in financial data associated with the identifiedcomparable public entities.
 13. The computer system of claim 12, whereinthe graphical user interface displays a trend associated changesassociated with the updated indicator for a predetermined time.
 14. Thecomputer system of claim 11, wherein the comparable public entities arealso displayed on the graphical user interface.
 15. The computer systemof claim 11, wherein an application programming interface protocolnotifies the server when any financial data of an identified comparablepublic entity has been modified.
 16. The computer system of claim 11,wherein the server is further configured to: crawl one or more databasesto identify one or more web documents associated with at least oneprivate entity; and display an indicator on the graphical user interfaceindicating identification of at least one web document associated withat least one private entity.
 17. The computer system of claim 11,wherein the graphical user interface further displays a value of eachprivate entity within the fund.
 18. The computer system of claim 11,wherein the graphical user interface further displays a second value ofthe fund received from a user computing device.
 19. The computer systemof claim 13, wherein the predetermined time is customizable by a userinteracting with the graphical user interface.
 20. The computer systemof claim 11, wherein the server retrieved the financial data in realtime.